In this paper, we reproduced the study of Abdollahpouri et al. [1]} on the unfairness of popularity bias from the user's perspective in the Movie domain, which we have applied to the book domain. Similar to the original paper, we divided all users into three groups (i.e., Niche, Diverse and Bestseller-focused) based on their level of interest in popular items. Our results on various state-of-the-art recommendation algorithms reveal that the most widely adopted algorithms fail to capture users' interest in unpopular items and recommend mostly popular items. Notably, the quality of recommendations received by users with a Diverse or Niche taste is significantly lower than that of users with Bestsellers taste, despite having a large profile size. Moreover, our experiments led to new observations and possible directions for future research.
First, we noticed that algorithms could differ significantly in their ability to capture users' tastes based on the domain. For instance, the NMF algorithm suffers from the unfairness of popularity bias in the music domain [2]} while offering an entirely fair recommendation in Book-Crossing dataset. A future research direction that would be interesting is identifying the underlying reason for the variance, in particular, which feature of the data (e.g., sparsity, average user interaction) plays the primary role in propagating the popularity bias.
Additionally, our results suggest that an underlying tradeoff exists between personalization and fairness of popularity bias in Diverse and Bestseller-focused groups, that is, algorithms with high personalization abilities tend to experience fairness issues. Thus, further research could be worthwhile into implementing a recommendation algorithm that can find the optimal tradeoff between personalization and the unfairness of popularity biases to enhance the system's overall effectiveness. Finally, it would be interesting to investigate popularity bias on other domains and algorithms such as session-based [3]}, content-based [4]}, or reinforcement learning-based recommendation [5]} methods, as well as incorporating further evaluation metrics such as novelty and coverage.
